Abstract
This paper proposed a new optimization algorithm-immune genetic algorithm (IGA), which is based on immune genetic theory of creatures and can simulate the immune system and its behaviour in organisms. Compared with common genetic algorithms, the IGA adopts the following techniques to improve the global searching ability and convergence speed: immune memory, immune selection, concentration control, niching technique, chaos production and metabolism. Several typical test functions are used to verify the excellent performances of the proposed IGA. Finally, the IGA is applied to optimize the parameters of a unified power flow controller (UPFC) to improve the stability of the New England Test Power System (NETPS). Numerical simulation results demonstrate the validity of the optimized UPFC controller.
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